Machine Learning Engineer

Job summary

Lead the full machine learning lifecycle, from data acquisition and model training through evaluation and production deployment. Architect efficient inference systems and scalable data and monitoring infrastructure, while partnering with research and engineering teams to deliver reliable AI product features.

Job details

Our company description Mission is a platform for hiring, vetting and managing software development talents. It enables our clients to connect with the world’s best talent to build mission-critical software products. About the Company This technology company operates a large-scale financial services platform originally established to serve international markets. The organization is expanding its ecosystem beyond its core fintech offerings to include savings, investments, and AI-native productivity tools. The company leverages artificial intelligence to build proactive applications designed to simplify daily digital tasks and workflows for a global user base, focusing on high reliability and autonomous task completion. About the Role As the Technical Lead for Machine Learning, you will oversee the execution layer of the company’s AI intelligence, transforming research into scalable, production-grade systems. This hands-on leadership role bridges the gap between model development and product implementation. You will own the entire model lifecycle, including data curation, training, evaluation, and deployment. Your focus will be on building reliable, high-performance systems that power proactive applications, ensuring long-running workflows and persistent context function seamlessly for millions of users worldwide. What You'll Do Lead the end-to-end development of machine learning systems, spanning data acquisition, training, and deployment. Architect high-performance inference systems to optimize latency, GPU utilization, and cost-efficiency. Design robust evaluation frameworks to measure model capability, safety, and real-world performance. Build scalable pipelines for processing high-quality synthetic and real-world training data. Establish production infrastructure for monitoring and continuously improving large-scale models. Partner with research and engineering teams to translate model capabilities into functional product features. Manage technical trade-offs to ensure rapid iteration and system reliability. What You Bring Proven experience building and shipping production-grade machine learning systems at scale. Deep expertise in modern large-model training, fine-tuning, and inference methodologies. Strong software engineering and systems design fundamentals. Technical proficiency with GPU-based workloads and infrastructure. Ability to navigate ambiguous technical problems and lead experimentation initiatives. Nice to Haves Proficiency with Python, PyTorch, or JAX. Experience managing large model failure modes in live environments. Experience writing production-grade code for high-reliability systems. Compensation & Benefits Benefits: Health and dental coverage, life insurance, retirement savings matching (US), and equity.

What you’ll do

Lead the full machine learning lifecycle, from data acquisition and model training through evaluation and production deployment. Architect efficient inference systems and scalable data and monitoring infrastructure, while partnering with research and engineering teams to deliver reliable AI product features.

Requirements

Candidates should have proven experience building and shipping production-grade machine learning systems at scale, with deep expertise in large-model training, fine-tuning, and inference. Strong software engineering and systems design skills, GPU infrastructure proficiency, and the ability to lead experimentation in ambiguous technical settings are required; Python, PyTorch, or JAX experience is a plus.

Benefits

• Health Coverage
• Dental Coverage
• Life Insurance
• Retirement Savings Matching
• Equity

Listed skills

• Machine learning · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Machine Learning
• Large-Model Training
• Model Fine-Tuning
• Inference Systems
• GPU Infrastructure
• Python
• PyTorch
• JAX
• Data Curation
• Synthetic Data Pipelines
• Model Evaluation
• Production Deployment
• Systems Design
• Performance Optimization
• Reliability Engineering
• Technical Leadership

Job areas

• Technology
• Software
• Engineering
• Data & Analytics
• Management & Leadership

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